Intensive Care Monitoring Research Articles

Serum total calcium levels as a non-linear predictor of in-hospital mortality in heart failure patients: insights from a retrospective cohort study

BackgroundCalcium is pivotal in the regulation of bodily homeostasis, with numerous studies highlighting its link to cardiovascular disease in the adult population. However, the relationship between serum calcium levels and the prognosis of heart failure (HF) patients is not clear. This study explored the association between serum total calcium (STC) and in-hospital mortality in patients with HF.MethodsClinical data of 1,176 patients with HF were obtained from the Multiparametric Intelligent Monitoring in Intensive Care III (MIMIC-III) database. The patients were categorized into STC quartiles, and baseline characteristics were comprehensively analyzed. Univariate and multivariate analyses were employed to identify factors associated with in-hospital mortality. To explore the non-linear relationship between STC and mortality, a two-piecewise linear regression model was applied. Subgroup analyses were conducted to identify potential confounding variables.ResultsIn this cohort, 159 (13.53%) patients experienced in-hospital mortality. Significant differences in various parameters were observed among STC quartiles. Univariate analysis identified numerous factors associated with mortality. Multivariate analysis confirmed STC as an independent predictor of in-hospital mortality, with a negative association persisting even after adjusting for confounding factors (odds ratio [OR]: 0.49, 95%CI: 0.32–0.76; P = 0.0016). Non-linear analysis revealed an inflection point at 8.41 mg/dL, below which the risk of in-hospital death significantly increased (OR: 0.26, 95%CI: 0.12–0.55; P = 0.0005). Subgroup analyses indicated a pronounced inverse association in patients without atrial fibrillation or chronic obstructive pulmonary disease, as well as those with a left ventricular ejection fraction ≤ 50%.ConclusionThis study identified STC as an independent predictor of in-hospital mortality in HF patients, with a non-linear relationship.

Blood stream infections in Stevens Johnson Syndrome and Toxic Epidermal Necrolysis: Risk factors and association with poor outcome.

Blood Stream Infections (BSI) are considered a significant cause of morbidity and mortality in patients with Stevens Johnson Syndrome (SJS) and Toxic Epidermal Necrolysis (TEN). We aimed to identify risk factors for BSI upon admission, highlight clinical and microbiological findings and ascertain the frequency of mortality in patients with BSI in SJS/TEN. A retrospective cross-sectional study over 12 years (2011-2022) was performed in the department of medicine at a tertiary care hospital in Pakistan. All patients admitted with the diagnosis of SJS or TEN were included: from the health information management system. We included clinical and microbiological details, reviewed medical charts, and filled out a predesigned proforma. A total of 100 patients were admitted with SJS or TEN. The majority (55%) were of age greater than 40 years and had female preponderance (57%). Sixty five patients had a prior history of using a precipitating drug. BSI was seen in 19 patients; 68.4% had a mono-microbial infection, while 31.5% had a poly-microbial infection. In total, 10 organisms were identified, Staphylococcus aureus being the most common isolate followed by Enterococcus. Twelve patients required intensive care monitoring while 33 patients had hospital stays of equal or more than seven days. The overall mortality rate was 15% while it was 60% in those with BSI. SCORTEN score of ≥4 had a significant impact on mortality (60% deaths). Vigilant monitoring and early detection of BSI in SJS/TEN patients, especially those presenting with high SCORTEN scores can enhance clinical outcomes.

Triglyceride-glucose index is associated with all-cause mortality in critically ill patients with alcohol use disorder: a retrospective cohort study

BackgroundThe relationship between Triglyceride-glucose (TyG) index and clinical outcomes in patients with alcohol use disorder (AUD) is unclear. The aim of this study was to evaluate the association between TyG index and all-cause mortality in critically ill patients with AUD.MethodsWe used data from the multi-parameter intelligent monitoring in intensive care IV (MIMIC-IV) database. The patients were equally divided into quartiles. Kaplan–Meier curves were used for survival analysis. The primary endpoint of the study was 28-day mortality, followed by 1-year mortality. We used Cox proportional hazard models to assess the relationship between TyG index and all-cause mortality at different endpoints.ResultsA total of 537 AUD patients were included. Using TyG value as a continuous variable (HR 1.460, 95% CI 1.121–1.903, p = 0.005) and categorical variable (HR 1.447–3.477 from Q2 to Q4, with Q1 as reference), elevated TyG value was significantly associated with increased 28-day mortality. TyG was positively associated with 1-year mortality in AUD patients with an HR of 1.295 (95% CI 1.011–1.659, p = 0.041).ConclusionTyG index is positively associated with different clinical outcomes of critically ill AUD patients.

Investigation of a Camera-Based Contactless Pulse Oximeter with Time-Division Multiplex Illumination Applied on Piglets for Neonatological Applications.

(1) Objective: This study aims to lay a foundation for noncontact intensive care monitoring of premature babies. (2) Methods: Arterial oxygen saturation and heart rate were measured using a monochrome camera and time-division multiplex controlled lighting at three different wavelengths (660 nm, 810 nm and 940 nm) on a piglet model. (3) Results: Using this camera system and our newly designed algorithm for further analysis, the detection of a heartbeat and the calculation of oxygen saturation were evaluated. In motionless individuals, heartbeat and respiration were separated clearly during light breathing and with only minor intervention. In this case, the mean difference between noncontact and contact saturation measurements was 0.7% (RMSE = 3.8%, MAE = 2.93%). (4) Conclusions: The new sensor was proven effective under ideal animal experimental conditions. The results allow a systematic improvement for the further development of contactless vital sign monitoring systems. The results presented here are a major step towards the development of an incubator with noncontact sensor systems for use in the neonatal intensive care unit.

Minimally invasive technique facilitates early extubation after cardiac surgery: a single-center retrospective study

BackgroundPostoperative time to extubation plays a role in prognosis after heart valve surgery; however, its exact impact has not been clarified. This study compared the postoperative outcomes of minimally invasive surgery and conventional sternotomy, focusing on early extubation and factors influencing prolonged mechanical ventilation.MethodsData from 744 patients who underwent heart valve surgery at the Zhejiang Provincial People’s Hospital between August 2019 and June 2022 were retrospectively analyzed. The outcomes in patients who underwent conventional median sternotomy (MS) and minimally invasive (MI) video-assisted thoracoscopic surgery were compared using inverse probability of treatment weighting (IPTW) and Kaplan–Meier curves. Clinical data, including surgical data, postoperative cardiac function, postoperative complications, and intensive care monitoring data, were analyzed.ResultsAfter propensity score matching and IPTW, 196 cases of conventional MS were compared with 196 cases of MI video-assisted thoracoscopic surgery. Compared to patients in the conventional MS group, those in the MI video-assisted thoracoscopic surgery group in the matched cohort had a higher early postoperative extubation rate (P < 0.01), reduced incidence of postoperative pleural effusion (P < 0.05), significantly shorter length of stay in the intensive care unit (P < 0.01), shorter overall length of hospital stay (P < 0.01), and lower total cost of hospitalization (P < 0.01).ConclusionsSuccessful early tracheal extubation is important for the intensive care management of patients after heart valve surgery. The advantages of MI video-assisted thoracoscopic surgery over conventional MS include significant reductions in the duration of use of mechanical ventilation support, reduced length of intensive care unit stay, reduced total length of hospitalization, and a favorable patient recovery rate.

Short-term vital parameter forecasting in the intensive care unit: A benchmark study leveraging data from patients after cardiothoracic surgery.

Patients in an Intensive Care Unit (ICU) are closely and continuously monitored, and many machine learning (ML) solutions have been proposed to predict specific outcomes like death, bleeding, or organ failure. Forecasting of vital parameters is a more general approach to ML-based patient monitoring, but the literature on its feasibility and robust benchmarks of achievable accuracy are scarce. We implemented five univariate statistical models (the naïve model, the Theta method, exponential smoothing, the autoregressive integrated moving average model, and an autoregressive single-layer neural network), two univariate neural networks (N-BEATS and N-HiTS), and two multivariate neural networks designed for sequential data (a recurrent neural network with gated recurrent unit, GRU, and a Transformer network) to produce forecasts for six vital parameters recorded at five-minute intervals during intensive care monitoring. Vital parameters were the diastolic, systolic, and mean arterial blood pressure, central venous pressure, peripheral oxygen saturation (measured by non-invasive pulse oximetry) and heart rate, and forecasts were made for 5 through 120 minutes into the future. Patients used in this study recovered from cardiothoracic surgery in an ICU. The patient cohort used for model development (n = 22,348) and internal testing (n = 2,483) originated from a heart center in Germany, while a patient sub-set from the eICU collaborative research database, an American multicenter ICU cohort, was used for external testing (n = 7,477). The GRU was the predominant method in this study. Uni- and multivariate neural network models proved to be superior to univariate statistical models across vital parameters and forecast horizons, and their advantage steadily became more pronounced for increasing forecast horizons. With this study, we established an extensive set of benchmarks for forecast performance in the ICU. Our findings suggest that supplying physicians with short-term forecasts of vital parameters in the ICU is feasible, and that multivariate neural networks are most suited for the task due to their ability to learn patterns across thousands of patients.

Lactate Monitoring in Intensive Care: A Comprehensive Review of Its Utility and Interpretation.

Lactate monitoring is critical in managing critically ill patients in intensive care settings. Elevated lactate levels often signify underlying metabolic disturbances such as tissue hypoxia, anaerobic metabolism, or impaired lactate clearance, which are prevalent in conditions like sepsis, shock, and trauma. Understanding the physiological basis of lactate production and its significance in clinical practice is essential for interpreting its diagnostic and prognostic value. This comprehensive review aims to explore the utility of lactate monitoring across various critical care scenarios. It provides an overview of lactate's metabolic pathways, methods of measurement, and the clinical implications of interpreting lactate levels in different contexts. Additionally, the review discusses current evidence on lactate-guided therapeutic interventions and highlights challenges and limitations to their application. By synthesizing the existing literature and clinical insights, this review aims to enhance the understanding of the role of lactate monitoring in assessing disease severity, guiding treatment strategies, and predicting outcomes in critically ill patients. Ultimately, this review underscores the importance of integrating lactate monitoring into routine clinical practice to optimize patient care and improve clinical outcomes in intensive care settings.

The association between ferritin levels and all-cause mortality in stroke patients.

The purpose of study was to describe the association between ferritin and all-cause mortality of cases with stroke. Clinical data derived from Multiparameter Intelligent Monitoring in Intensive Care were analyzed. The primary endpoint was 30-day mortality. The potential prognostic roles of Ferritin L were analyzed by Cox proportional hazard models. The independent prognostic roles of Ferritin L in the cases were analyzed by smooth curve fitting. Concerning 30-day mortality, the HR (95% CI) for a high Ferritin (≥373) was 1.925 (1.298, 2.854; p = 0.00113), compared to a low ferritin (< 373). After adjusting for multiple confounders, the HR (95% CI) for a high Ferritin (≥373) was 1.782 (1.126, 2.820; p = 0.01367), compared to a low Ferritin (< 373). A non-linear association between Ferritin and 30-day mortality was found. Using recursive algorithm and two-piecewise linear regression model, inflection point (IP) was calculated, which was 2,204. On the left side of the IP, there was a positive relationship between Ferritin and 30-day mortality, and the effect size, 95% CI and p value were 1.0006 (1.0004, 1.0009) p < 0.0001, respectively. On the right of the IP, the effect size, 95% CI and p value were 1.0000 (1.0000, 1.0000) and 0.3107, respectively. Ferritin was associated with increased risk of stroke; it is important to further examine the association if the increased uric acid would increase the outcome of stroke in a longitudinal study. The non-linear relationship between Ferritin and all-cause mortality of stroke was observed. Ferritin was a risk factor for the outcome of stroke when ferritin was <2204.

Comparative study on the predictive value of TG/HDL-C, TyG and TyG-BMI indices for 5-year mortality in critically ill patients with chronic heart failure: a retrospective study

BackgroundThe triglyceride glucose (TyG) index, TyG-body mass index (TyG-BMI), and triglyceride-density lipoprotein cholesterol ratio (TG/HDL-C) are substitute indicators for insulin resistance (IR). This study aimed to compare the predictive value of these indicators for 5-year mortality in critically ill patients with chronic heart failure (CHF).MethodsCritically ill patients with CHF were identified from the Multiparameter Intelligent Monitoring in Intensive Care (MIMIC) III and IV databases. The primary outcome was 5-year mortality. The relationship between the three indices and mortality risk was determined using multivariate Cox proportional hazards models, Kaplan–Meier (K‒M) analysis and restricted cubic splines analysis. A receiver operating characteristic (ROC) curve was generated to compare the ability of the three indices to predict mortality. Finally, whether the IR indices would further increase the predictive ability of the basic model including baseline variables with a significance level between survivors and non-survivors was evaluated by ROC curve.ResultsAltogether, 1329 patients with CHF were identified from the databases. Cox proportional hazards models indicated that the TyG index was independently associated with an elevated risk of 5-year mortality (hazard ratio [HR], 1.56; 95% confidence interval [CI] 1.29–1.9), while the TyG-BMI index and TG/HDL-C level were significantly associated with 5-year mortality, with an HR (95% CI) of 1.002 (1.000–1.003) and 1.01 (1.00–1.03), respectively. The K–M analysis revealed that the cumulative incidence of all-cause 5-year death increased with increasing quartiles of the TyG index, TyG-BMI index, or TG/HDL-C ratio. According to the ROC curve, the TyG index outperformed the TyG-BMI and TG/HDL-C ratio at predicting all-cause 5-year mortality (0.608 [0.571–0.645] vs. 0.558 [0.522–0.594] vs. 0.561 [0.524–0.598]). The effect of the TyG index on all-cause mortality was consistent across subgroups, with no significant interaction with randomized factors. Furthermore, adding the TyG index to the basic model for 5-year mortality improved its predictive ability (area under the curve, 0.762 for the basic model vs. 0.769 for the basic model + TyG index); however, the difference was not statistically significant.ConclusionAs continuous variables, all three indices were significantly associated with 5-year mortality risk in critically ill patients with CHF. Although these IR indices did not improve the predictive power of the basic model in patients with CHF, the TyG index appears to be the most promising index (vs. TyG-BMI and TG/HDL-C ratio) for prevention and risk stratification in critically ill patients with CHF.

Heparin is an effective treatment for preventing liver failure after hepatectomy.

Posthepatectomy liver failure (PHLF) is one of the most important causes of death following liver resection. Heparin, an established anticoagulant, can protect liver function through a number of mechanisms, and thus, prevent liver failure. To look at the safety and efficacy of heparin in preventing hepatic dysfunction after hepatectomy. The data was extracted from Multiparameter Intelligent Monitoring in Intensive Care III (MIMIC-III) v1. 4 pinpointed patients who had undergone hepatectomy for liver cancer, subdividing them into two cohorts: Those who were injected with heparin and those who were not. The statistical evaluations used were unpaired t-tests, Mann-Whitney U tests, chi-square tests, and Fisher's exact tests to assess the effect of heparin administration on PHLF, duration of intensive care unit (ICU) stay, need for mechanical ventilation, use of continuous renal replacement therapy (CRRT), incidence of hypoxemia, development of acute kidney injury, and ICU mortality. Logistic regression was utilized to analyze the factors related to PHLF, with propensity score matching (PSM) aiming to balance the preoperative disparities between the two groups. In this study, 1388 patients who underwent liver cancer hepatectomy were analyzed. PSM yielded 213 matched pairs from the heparin-treated and control groups. Initial univariate analyses indicated that heparin potentially reduces the risk of PHLF in both matched and unmatched samples. Further analysis in the matched cohorts confirmed a significant association, with heparin reducing the risk of PHLF (odds ratio: 0.518; 95% confidence interval: 0.295-0.910; P = 0.022). Additionally, heparin treatment correlated with improved short-term postoperative outcomes such as reduced ICU stay durations, diminished requirements for respiratory support and CRRT, and lower incidences of hypoxemia and ICU mortality. Liver failure is an important hazard following hepatic surgery. During ICU care heparin administration has been proved to decrease the occurrence of hepatectomy induced liver failure. This indicates that heparin may provide a hopeful option for controlling PHLF.

Radiological and Biochemical Parameters in Assessing Acute Pancreatitis Severity: A Comprehensive Review.

Acute pancreatitis is a dynamic inflammatory condition of the pancreas with a spectrum ranging from mild to severe. Early and accurate assessment of disease severity is crucial for guiding clinical management and improving patient outcomes. This comprehensive review explores the role of radiological and biochemical parameters in assessing the severity of acute pancreatitis. Radiological imaging modalities, including computed tomography (CT), magnetic resonance imaging (MRI), and ultrasound (US), play a pivotal role in identifying key features, such as pancreatic necrosis and peripancreatic fluid collections, indicative of severe disease. Additionally, serum markers such as amylase, lipase, and C-reactive protein (CRP) provide valuable prognostic information and aid in risk stratification. Integrating radiological and biochemical parameters allows for a multidimensional evaluation of disease severity, enabling clinicians to make informed decisions regarding patient management. Early identification of severe cases facilitates timely interventions, including intensive care monitoring, nutritional support, and potential surgical interventions. Despite significant advancements in the field, there remain areas for further research, including the validation of emerging imaging techniques and biomarkers and the exploration of personalized management approaches. Addressing these research gaps can enhance our understanding of acute pancreatitis and ultimately improve patient care and outcomes.

Comorbidities, acute kidney injury and long-term mortality in elderly patients hospitalized because of hip fracture: a moderation analysis

IntroductionFemoral fractures in elderly individuals present significant health challenges, often leading to increased morbidity and mortality. Acute kidney injury (AKI) during hospitalization further complicates outcomes, yet the interaction between AKI severity and comorbidities, as quantified by the Charlson Comorbidity Index (CCI), remains poorly understood in this population. This study aimed to assess the associations between AKI severity and the CCI and between AKI severity and one-year mortality postfemoral fracture in elderly patients.MethodologyThis study utilized data from the Multiparameter Intelligent Monitoring in Intensive Care (MIMIC-IV) database and focused on elderly patients (> 65 years) admitted with hip fractures. Patients were categorized based on AKI stage according to the KDIGO criteria and CCI scores. The primary outcome assessed was all-cause mortality one year after hospital discharge. The statistical analyses included logistic regression, Cox proportional hazards regression and moderation analysis with the Johnson–Neyman technique to evaluate associations between AKI and long-term mortality and between the CCI and long-term mortality.ResultsThe analysis included 1,955 patients and revealed that severe AKI (stages 2 and 3) was independently associated with increased one-year mortality. Notably, the CCI moderated these associations significantly. A lower CCI score was significantly correlated with greater mortality in patients with severe AKI. The impact of severe AKI was greater for those with a CCI as low as 3, more than doubling the observed one-year mortality rate. In contrast, higher CCI scores (≥8) did not significantly impact mortality. Sensitivity analyses supported these findings, underscoring the robustness of the observed associations.ConclusionThis study elucidates the complex interplay between AKI severity and comorbidities and long-term mortality in elderly hip fracture patients. These findings underscore the importance of considering both AKI severity and comorbidity burden in prognostic assessments and intervention strategies for this vulnerable population. Targeted interventions tailored to individual risk profiles may help mitigate the impact of AKI on mortality outcomes, ultimately improving patient care and outcomes. Further research is warranted to explore the underlying mechanisms involved and refine risk stratification approaches in this population.

Prolonged elevated heart rate and 90-Day mortality in acute pancreatitis

Prolonged elevated heart rate (peHR) is recognized as a risk factor for poor prognosis among critically ill patients. However, there is currently a lack of studies investigating the association between peHR and patients with acute pancreatitis. Multiparameter Intelligent Monitoring in Intensive Care IV (MIMIC-IV) database was used to identify patients with acute pancreatitis. PeHR was defined as a heart rate exceeding 100 beats per minute for at least 11 out of 12 consecutive hours. Cox regression analysis was used to assess the association between peHR and the 90-Day mortality. A total of 364 patients (48.9%) experienced a peHR episode. The 90-day mortality was 25%. PeHR is an independent risk factor for 90-day mortality (HR, 1.98; 95% CI 1.53–2.56; P < 0.001). KM survival curves exhibited a significant decrease in the survival rate at 90 days among patients who experienced a peHR episode (P < 0.001, 84.5% vs. 65.1%). We revealed a significant association of peHR with decreased survival in a large cohort of ICU patients with acute pancreatitis.

IoT driven joint compressed sensing and shallow learning approach for ECG signal-reconstruction

Because biological signal transmission in real time might be very demanding, cloud and internet of things (IoT) infrastructure are required. To do this, the main component of the signal serves as the focal point of a reconstruction strategy that has been developed. The input is transferred to the intended destination once it has been encoded. Security is an important consideration that must not be disregarded. For long-term healthcare monitoring via lightweight wireless networks, electrocardiogram (ECG) compression is a major difficulty. Reducing energy consumption in wireless data transmission and precisely calculating error rates for data reconstruction are two essential components of compressed sensing. The application of effective encoding methods is crucial for these considerations. We present multi-task compressed sensing (MT-CS), a unique method for compressing ECG data. When used to wireless network systems with several embedded sensors, this technique is quite effective. From the ECG data, the model learns the fundamental adaptive properties needed for correlation. We use the multiparameter intelligent monitoring in intensive care (MIMIC-II) dataset to investigate the performance of the suggested MT-CS reconstruction technique in order to assess its strength and application. In comparison to current compressed sensing methods, the simulation results demonstrate that the suggested reconstruction methodology utilizing MT-CS generates high-quality reconstruction signals with fewer observations.

Outcomes of carotid endarterectomy: Insights from a single-center retrospective cohort study.

To analyze the outcomes of carotid endarterectomy in individuals with carotid artery stenosis in the context of a tertiary care center. We carried out a retrospective cohort investigation between 2015-2022. Patient data includes demographics, risk factors, preoperative medications, and operative details. The primary outcomes were 30-day postoperative stroke and mortality rates, while the secondary outcome of the study was to assess the morbidity of the procedure. The mean age of the 54 patients was 66.9±9.88 years, and 57.4% were men. The 30-day stroke rate was 3.7%, and the mortality rate was 1.9%. Most patients did not develop postoperative complications; however, surgical site hematoma was the most common complication encountered (12.9%). Long-term follow-up showed disease regression in 68.5% of patients, with a minority of patients developing ipsilateral restenosis. Admission to an intensive care monitoring unit was the only independent predictor of postoperative complications. This study provided insights into the outcomes of carotid endarterectomy in patients with carotid artery stenosis, emphasizing the importance of careful patient selection and postoperative monitoring. Perioperative risks, including stroke and mortality, were within acceptable limits. Further research incorporating structured and non-structured data for predictive analyses, should explore refining patient profiling and optimizing treatment approaches for different carotid artery stenosis clinical and morphological presentations.

Evaluating the utility of quantitative pupillometry in a neuro-critical care setting for the monitoring of intracranial pressure: A prospective cohort study

IntroductionAssessment of the pupillary light reflex (PLR) is key in intensive care monitoring of neurosurgical patients, particularly for monitoring intracranial pressure (ICP). Quantitative pupillometry using a handheld pupillometer is a reliable method for PLR assessment. However, many variables are derived from such devices. We therefore aimed to assess the performance of these variables at monitoring ICP. MethodsSedated patients admitted to neurocritical care in a tertiary neurosurgical centre with invasive ICP monitoring were included. Hourly measurement of ICP, subjective pupillometry (SP) using a pen torch device, and quantitative pupillometry (QP) using a handheld pupillometer were performed. Results561 paired ICP, SP and QP pupillary observations from nine patients were obtained (1122 total pupillary observations). SP and QP had a moderate concordance for pupillary size (κ=0.62). SP performed poorly at detecting pupillary size changes (sensitivity=24%). In 40 (3.6%) observations, SP failed to detect a pupillary response whereas QP did. Moderate correlations with ICP were detected for maximum constriction velocity (MCV), dilation velocity (DV), and percentage change in pupillary diameter (%C). Discriminatory ability at an ICP threshold of >22 mmHg was moderate for MCV (AUC=0.631), DV (AUC=0.616), %C (AUC=0.602), and pupillary maximum size (AUC=0.625). ConclusionQP is superior to SP at monitoring pupillary reactivity and changes to pupillary size. Although effect sizes were moderate to weak across assessed variables, our data indicates MCV and %C as the most sensitive variables for monitoring ICP. Further study is required to validate these findings and to establish normal range cut-offs for clinical use.

Association between inflammation markers and all-cause mortality in critical ill patients with atrial fibrillation: Analysis of the Multi-Parameter Intelligent Monitoring in Intensive Care (MIMIC-IV) database

BackgroundInflammation is related to cardiovascular disease. Among the many inflammatory markers, neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and systemic immune-inflammatory index (SII) were considered as novel predictors for atherosclerosis outcomes. We aimed to investigate the impact of these inflammatory markers on the prognosis of patients with atrial fibrillation (AF). MethodsWe obtained data on AF patients from the Medical Information Mart for Intensive Care (MIMIC)-IV database. These patients were classified into two groups based on their survival status within 30 days. Then, they were divided into three groups based on the tertile of baseline NLR, PLR, and SII, respectively. We comprehensively explored the relationship between those inflammatory indicators and all-cause mortality in patients with AF by Kaplan-Meier analysis, multivariate Cox regression analysis, receiver operating characteristic (ROC) analyses, restricted cubic spline regression (RCS), and subgroup analysis. ResultsA total of 4562 patients with AF were included. Statistically significant differences were found between survivor and non-survivor groups for NLR, PLR and SII. Patients in the high tertile of the NLR had a higher mortality rate than those in the low and intermediate tertiles, as did patients in the PLR and the SII. NLR, PLR and SII were independently associated with increased risk of all-cause mortality. RCS showed that the 30-day and 365-day risk of death were linearly associated with increases in NLR, PLR, and SII, respectively. ConclusionNLR, PLR, and SII have the potential to be used as indicators for stratifying the risk of mortality in critically ill patients with AF.

Mortalité maternelle par accident vasculaire cérébral en France 2016–2018

Between 2016 and 2018, twenty maternal deaths were associated with a stroke. The 20 deaths whose main cause was stroke represent 7.4% of all maternal deaths, i.e. a maternal mortality ratio (MMR) of 0.9 per 100,000 live births (95%CI 0.6–1.3). Among the 20 stroke deaths, it was hemorrhagic in 17 cases (85%), ischemic in 2 cases, and due to thrombophlebitis in 1 case. Stroke occurred during pregnancy in 8 women (40%) – one case before 12 weeks, 3 cases between 28 and 32 weeks, and 4 cases between 34 and 40 weeks; in 3 cases the stroke occurred intrapartum, and for the other 9 cases (45%) the stroke occurred postpartum between Day 1 and Day 15. Care was assessed as non-optimal in 10/19 (56%) of cases but mortality as possibly avoidable in 24% of cases (4/17 cases with conclusion established by the CNEMM) and not established in two cases. The potentially improvable elements identified were a delay in carrying out initial brain imaging in three cases (one case antepartum, two cases postpartum) and insufficient hemodynamic monitoring in intensive care in one case.

A Deep Learning Framework for Predicting Patient Decannulation on Extracorporeal Membrane Oxygenation Devices: Development and Model Analysis Study.

Venovenous extracorporeal membrane oxygenation (VV-ECMO) is a therapy for patients with refractory respiratory failure. The decision to decannulate someone from extracorporeal membrane oxygenation (ECMO) often involves weaning trials and clinical intuition. To date, there are limited prognostication metrics to guide clinical decision-making to determine which patients will be successfully weaned and decannulated. This study aims to assist clinicians with the decision to decannulate a patient from ECMO, using Continuous Evaluation of VV-ECMO Outcomes (CEVVO), a deep learning-based model for predicting success of decannulation in patients supported on VV-ECMO. The running metric may be applied daily to categorize patients into high-risk and low-risk groups. Using these data, providers may consider initiating a weaning trial based on their expertise and CEVVO. Data were collected from 118 patients supported with VV-ECMO at the Columbia University Irving Medical Center. Using a long short-term memory-based network, CEVVO is the first model capable of integrating discrete clinical information with continuous data collected from an ECMO device. A total of 12 sets of 5-fold cross validations were conducted to assess the performance, which was measured using the area under the receiver operating characteristic curve (AUROC) and average precision (AP). To translate the predicted values into a clinically useful metric, the model results were calibrated and stratified into risk groups, ranging from 0 (high risk) to 3 (low risk). To further investigate the performance edge of CEVVO, 2 synthetic data sets were generated using Gaussian process regression. The first data set preserved the long-term dependency of the patient data set, whereas the second did not. CEVVO demonstrated consistently superior classification performance compared with contemporary models (P<.001 and P=.04 compared with the next highest AUROC and AP). Although the model's patient-by-patient predictive power may be too low to be integrated into a clinical setting (AUROC 95% CI 0.6822-0.7055; AP 95% CI 0.8515-0.8682), the patient risk classification system displayed greater potential. When measured at 72 hours, the high-risk group had a successful decannulation rate of 58% (7/12), whereas the low-risk group had a successful decannulation rate of 92% (11/12; P=.04). When measured at 96 hours, the high- and low-risk groups had a successful decannulation rate of 54% (6/11) and 100% (9/9), respectively (P=.01). We hypothesized that the improved performance of CEVVO was owing to its ability to efficiently capture transient temporal patterns. Indeed, CEVVO exhibited improved performance on synthetic data with inherent temporal dependencies (P<.001) compared with logistic regression and a dense neural network. The ability to interpret and integrate large data sets is paramount for creating accurate models capable of assisting clinicians in risk stratifying patients supported on VV-ECMO. Our framework may guide future incorporation of CEVVO into more comprehensive intensive care monitoring systems.

Screening and treatment of obstructive sleep apnea pre and post bariatric surgery reduces the need for post-operative intensive care monitoring and length of hospital stay

Screening and treatment of obstructive sleep apnea pre and post bariatric surgery reduces the need for post-operative intensive care monitoring and length of hospital stay